ggNetView : an R package for network analysis and visualization.
It provides flexible and publication-ready tools for exploring complex biological and ecological networks.
You can install the development version of ggNetView from GitHub with:
# install.packages("devtools")
devtools::install_github("Jiawang1209/ggNetView")
or
# install.packages("pak")
pak::pak("Jiawang1209/ggNetView")
library(ggNetView)
You can load raw matrix
data("otu_tab")
otu_tab[1:5, 1:5]
#> KO1 KO2 KO3 KO4 KO5
#> ASV_1 1113 1968 816 1372 1062
#> ASV_2 1922 1227 2355 2218 2885
#> ASV_3 568 460 899 902 1226
#> ASV_4 1433 400 535 759 1287
#> ASV_6 882 673 819 888 1475
You can load rarely matrix
data("otu_rare")
otu_tab[1:5, 1:5]
#> KO1 KO2 KO3 KO4 KO5
#> ASV_1 1113 1968 816 1372 1062
#> ASV_2 1922 1227 2355 2218 2885
#> ASV_3 568 460 899 902 1226
#> ASV_4 1433 400 535 759 1287
#> ASV_6 882 673 819 888 1475
data("otu_rare_relative")
otu_rare_relative[1:5, 1:5]
#> KO1 KO2 KO3 KO4 KO5
#> ASV_1 0.03306667 0.05453333 0.02013333 0.03613333 0.02686667
#> ASV_2 0.05750000 0.03393333 0.06046667 0.05810000 0.07320000
#> ASV_3 0.01733333 0.01296667 0.02290000 0.02336667 0.03106667
#> ASV_4 0.04266667 0.01093333 0.01416667 0.01933333 0.03346667
#> ASV_6 0.02646667 0.01856667 0.02110000 0.02353333 0.03806667
You can load node annotation
data("tax_tab")
tax_tab[1:5, 1:5]
#> OTUID Kingdom Phylum Class Order
#> 1 ASV_2 Archaea Thaumarchaeota Unassigned Nitrososphaerales
#> 2 ASV_3 Bacteria Verrucomicrobia Spartobacteria Unassigned
#> 3 ASV_31 Bacteria Actinobacteria Actinobacteria Actinomycetales
#> 4 ASV_27 Archaea Thaumarchaeota Unassigned Nitrososphaerales
#> 5 ASV_9 Bacteria Unassigned Unassigned Unassigned
obj <- build_graph_from_mat(
mat = otu_rare_relative,
transfrom.method = "none",
method = "WGCNA",
cor.method = "pearson",
proc = "BH",
r.threshold = 0.7,
p.threshold = 0.05,
node_annotation = tax_tab
)
#>
p1 <- ggNetView(
graph_obj = obj,
layout = "gephi",
layout.module = "adjacent",
group.by = "Modularity",
pointsize = c(1, 5),
center = F,
jitter = F,
mapping_line = F,
shrink = 0.9,
linealpha = 0.2,
linecolor = "#d9d9d9"
)
#> Coordinate system already present.
#> ℹ Adding new coordinate system, which will replace the existing one.
#> Scale for fill is already present.
#> Adding another scale for fill, which will replace the existing scale.
p1
p2 <- ggNetView(
graph_obj = obj,
layout = "gephi",
layout.module = "random",
group.by = "Modularity",
pointsize = c(1, 5),
center = F,
jitter = TRUE,
jitter_sd = 0.15,
mapping_line = TRUE,
shrink = 0.9,
linealpha = 0.2,
linecolor = "#d9d9d9",
add_outer = T,
label = T
)
#> Coordinate system already present.
#> ℹ Adding new coordinate system, which will replace the existing one.
#> Scale for fill is already present.
#> Adding another scale for fill, which will replace the existing scale.
#> Large array (1000 rows x 1001 columns x 17 images) broken into 4 pieces to avoid memory limits
#>
#> Each piece of the raster consists of 708 rows and 708 columns
#>
#> Coordinate system already present.
#> ℹ Adding new coordinate system, which will replace the existing one.
p2
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
ggsave(file = "Output/p1.pdf",
plot = p1,
height = 10,
width = 10)
ggsave(file = "Output/p2.pdf",
plot = p2,
height = 10,
width = 10)
If you use ggNetView in your research, please cite:
Yue Liu (2025). ggNetView: An R package for complex biological and ecological network analysis and visualization. R package version 0.1.0.
https://github.com/Jiawang1209/ggNetView